PROJECT SUMMARY ABSTRACT ? Data Management and Statistics Core The Data Management and Statistics Core (DMSC) supports the mission of the UC Davis (UCD) Alzheimer's Disease Center (ADC) by maintaining a secure, central database; providing statistical analysis of the complex longitudinal data; and collaborating closely with the other Cores and investigators on research and mentoring. The DMSC team is internationally recognized for intellectual leadership that contributes to scientific advances, collaborations, and education and training. Key DMSC innovations in the current grant cycle (07/11?06/16) have included: 1) Refinement and expansion of the functionality of the state-of-the art database management system (Lava) that was developed and is maintained and extended through a cross-ADC collaboration. 2) Application of reproducible research principles and methods to dynamically generate reports that integrate database, data analysis, and written and graphic presentation of results. 3) Implementation of parallel process longitudinal models for correlated outcomes and predictors. 4) Development of analytic methods for high dimension imaging and biomarker data. 5) Creation of latent variable models to define, measure, and capture dynamic change in complex but important constructs like cognitive and functional reserve. 6) Statistical harmonization of cognitive test batteries to enable merging of datasets from different studies. 7) Local, national, and international leadership in collaborative science efforts and in training the next generation of researchers. In the next grant cycle, the DMSC will help to advance our understanding of factors that influence heterogeneity of cognitive trajectories across the spectrum from normal cognition through mild cognitive impairment and into dementia in four areas. 1) We will oversee data management for the UCD ADC and its Cores, providing a secure web-based central resource. 2) We will carry out statistical analyses for interdisciplinary ADC research on pathogenesis, diagnosis, treatment, and prevention of age-related degenerative disease. 3) We will develop new statistical methodology as needed to support the analysis of ADC's complex data. 4) We will work closely with the Research Education Component to mentor and train the next generation of AD researchers in study design, analysis, and presentation of results.